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By design,
the research directions of the MRO Lab
are aligned with my personal research interests.
The general focus is on perception systems for mobile robots that operate in unconstrained, dynamic environments.
A major aim is to integrate research results timely in industrial demonstrators.
More specifically,
my research addresses
Rich 3D Perception,
Robot Vision and
Mobile Robot Olfaction.

M.Sc. in Physics from Konstanz University in 1998. Thesis work on the "Structure of (C60)n+-Clusters using Gas Phase Ion Chromatography".

Teaching and Supervision

I have ten years teaching experience at university level,
including postgraduate courses in machine learning
and undergraduate courses in digital image processing, robotics,
web development with J2EE and web client programming;
supervision of many B.Sc. and M.Sc. thesis projects;
and supervision of 24 Ph.D. students (18 as primary supervisor).

Invited presentation at the Department Mechanical Engineering, Measurement and Control at the University of Kassel,
Transport and Inspection Robots at the AASS MR&O Lab,
14 February 2012.
Presentation:
[PDF (6.2 MB)]

Publications

I am author/co-author of about 150 refereed conference papers and journal publications.

A selection of papers by topic and a full list of my publications follows below.
You can also find my publications together with citation information on
Research Gate and
my Google Scholar Profile.
Please note that Google Scholar has known issues with erroneous metadata and inflated counts due to self-citations (see [Homepage of Gaurav S. Sukhatme]).

This paper is about improving the quality of 3D data.
We are especially considering 3D data obtained with structured light sensors such as the Kinect,
which was originally developed for the Xbox and is nowadays also often used in robotics.

Raw data obtained with an Asus Xtion Pro sensor are shown in the figure on the right.
By clicking on the image you can see the result of the proposed algorithm:
much of the noise present in the data is effectively removed.

Working on the denoised data is intuitively expected to lead to improved performance
if denoising does not smooth out important geometric structures too much.
Applications that could benefit include object detection, robot localization, and loop detection.
In this paper we analyse the performance boost
that can be achieved with the proposed denoising step
in approaches that use local shape features.
Local features are an effective means to deal with large amounts of data,
such as 2D images, video data or 3D point clouds.
The idea is
to detect key points with an algorithm that tries to identify distinctive and reliably recognizable regions
and to describe them with a single vector - a feature descriptor.
To avoid the computational cost of working with all the data,
local feature algorithms just consider a sparse set of feature descriptors to represent scenes or objects.
The class of local feature-based approaches is known to handle occlusions well and can be very efficient if
(1) the keypoint detector reproducably identifies the same regions of the real world,
especially under changes of the angle of view,
and
(2) the feature descriptors are discriminative enough to allow for reliable matching.
This means that feature vectors
which belong to the same part of the real world
should be similar to each other and dissimilar to all other feature descriptors in a scene
when computed for a range of viewing angles.

In this work we analyse
whether the stability of state of the art 3D key point detectors improves, and
whether the descriptors of local shape features can be better matched after the proposed denoising step.

Denoising Approach (TDSF Mapping Denoising)

We propose to remove noise by building a local 3D map
through integration of a sequence of raw data frames into a joint data structure.
We use the 3D signed distance map representation,
which allows to retrieve accurately all object surfaces in the scene;
the SDF Tracker algorithm
(see the paper [Canelhas et al., IROS-2013a])
to build a local 3D map;
and
then re-sample from this map to obtain a denoised 3D point cloud (a result can be seen in the figure to the right).

Results

We analyse keypoint detection stability and feature descriptor matching performance
on a data-set related to a real-world automated logistics application (the data were collected within the RobLog Project).
Specifically, we compare the effect of the proposed TSDF map denoising to two state of the art single frame image denoising methods.
For the stability of a representative keypoint detector (NARF),
we find an improvement of about 20% with the single frame image denoising methods
and another 20% improvement on top with TSDF map denoising.
Regarding the matching performance of five local shape features,
selected to represent the current state of the art,
we observe a consistent, formidable improvement across all classes of descriptors.

Key Contributions

Suggestion of a new application for SDF tracking and modelling: denoising, particularly to improve keypoint stability and feature description.